Prediction of BLEVE mechanical energy by implementation of artificial neural network

Hemmatian, Behrouz; Casal, Joaquim; Planas, Eulàlia; Hemmatian, B.; Rashtchian, Davood

doi:10.1016/j.jlp.2019.104021

Cited by 17 publications

(3 citation statements)

References 25 publications

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“…In the literature, Hemmatian et al 2020 has his work in conjunction with a related analysis. The aim of this research is to use an artificial neural network to estimate BLEVE mechanical energy [30].…”

Section: Discussionmentioning

confidence: 99%

BLEVE risk effect estimation using the Levenberg-Marquardt algorithm in an artificial neural network model

BARIŞIK¹

2022

Sigma J Eng Nat Sci - Sigma Müh Fen Bil Derg

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With the advancement of sciences such as machine learning, deep learning, and artificial intelligence, various algorithms are designed and developed. Learning and application models based on data types such as sensor data and databases of computers are made. The BLEVE effects, one of the most common types of fire in industries, are predicted using the Levenberg-Marquardt algorithm, which has become increasingly popular in recent years. Here, the equations in the TNO (Toegepast Natuurwetenschappelijk Onderzoek-Netherlands Applied Scientific Research Organization) (YellowBook Static) model of BLEVE are used.The aim of this research is to use artificial neural networks to predict the risk size of the BLEVE event. All the results from the TNO model of BLEVE effects were estimated using an artificial neural network model. Without utilizing equations, outcomes that are close to true results could be estimated in this method. Furthermore, findings were acquired fast, with linear outcomes in many settings. For this reason, the study's necessity and significance have shifted in this direction. Sixteen TNO model equations from BLEVE are applied, and heat flux values are calculated as a result. As a result of the studies, the BLEVE effects were predicted by the artificial neural network model created using the Levenberg-Marquardt algorithm. It is seen that the estimated results and the actual results calculated are close to each other. The statistical values between the predicted results of the artificial neural network model created by the Levenberg-Marquardt algorithm and the actual results were examined. The average relative error between the last stage of the BLEVE model created with ANN and the actual values was 3.34%. In addition, it has been observed that when the training iteration is increased within the algorithm, it gets closer to the real results, and statistical values such as standard error decrease even more. In other words, the computation was done by increasing the iteration sequence processing cycle of the result estimations and repeated with the number of training iterations in order to better evaluate the network's performance. It has been observed that as the number of iterations increases, closer and more realistic results emerge.

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Section: Discussionmentioning

confidence: 99%

BLEVE risk effect estimation using the Levenberg-Marquardt algorithm in an artificial neural network model

BARIŞIK¹

2022

Sigma J Eng Nat Sci - Sigma Müh Fen Bil Derg

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“…The material parameters of STEEL 4340 high-quality carbon structural steel are shown in Table 3. 26,27…”

Section: Finite Element Numerical Simulationmentioning

confidence: 99%

Influence of sensor installation tilt angle on explosion shock wave pressure test

Wang,

Kong

2023

Measurement and Control

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The surface reflection pressure sensor installation flatness will directly lead to the change of the explosion shock wave pressure propagation and distribution law, affecting the test results accuracy, and the test data cannot accurately evaluate the ammunition explosion damage power. In this study, the numerical simulation model of the explosion shock wave pressure propagation and distribution law was established by using the explosive mechanics simulation software, and the pressure distribution law was studied when the sensors installation angles were 0°, 4°, 8°, 12°, −4°, −8°, and −12° respectively. Combined with analysis of the pressure peak value and the pressure evolution nephogram at different measuring points, it is clarified that the positive tilt angle of the sensor installation has an enhancing effect on the pressure peak, while the negative tilt angle has a attenuation effect on the pressure peak. Based on the calculation function formula of the surface reflected pressure peak value in the national defense engineering design code, the surface reflected pressure peak value correction function formula is established by introducing the sensor installation angle correction effect. This study results provide a theoretical basis for the design of ammunition explosion shock wave pressure engineering test scheme and the test data validity verification.

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“…The material of the sensor mounting plate is STEEL-1006 in the AUTODYN material library, the Liner equation of state is used, and the damage model adopts the Johnson-Cook constitutive model that is suitable for the description of the mechanical behavior of metal materials under impact and explosion loads. The expression is [9][10]:…”

Section: Materials State Equation and Parametersmentioning

confidence: 99%

The influence of sensor installation angle on explosive shock wave overpressure test

Wang¹,

Kong²

2021

Vib. proced.

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In the engineering measurement of explosion shock wave pressure, the installation method of shock wave pressure sensor is of great significance to accurately obtain shock wave pressure. Based on the actual shooting range explosion shock wave overpressure test program, this paper uses AUTODYN to establish a finite element numerical simulation model. The distance from the ground reflection pressure sensor to the explosion center is 3 m, 4 m, 5 m, and 6 m. The shock wave overpressure data in the range of 8° and 10° are acquired and analyzed. The analysis results show that the shock wave overpressure obtained when the sensor installation angle is absolute zero is the ideal value. When the sensor installation plate is within a certain range, the overpressure value of the explosion shock wave increases with the increase of the installation angle of the plate, and the installation angle of the sensor is positively correlated with the overpressure of the explosion shock wave.

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Prediction of BLEVE mechanical energy by implementation of artificial neural network

Cited by 17 publications

References 25 publications

BLEVE risk effect estimation using the Levenberg-Marquardt algorithm in an artificial neural network model

BLEVE risk effect estimation using the Levenberg-Marquardt algorithm in an artificial neural network model

Influence of sensor installation tilt angle on explosion shock wave pressure test

The influence of sensor installation angle on explosive shock wave overpressure test

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